SoroushVahidi/scidocs huggingface dataset:
Performance comparison of adder architectures on 28nm FPGA
(2016)

Adders are one of the most desirable entities in processor data path architecture. Since long, VLSI engineers are working towards optimization and miniaturization of adder architectures to ultimately improve the performance of processors. As the technology is scaling down, challenges towards optimization are also increasing. In this paper we present the implementation of various 16 bit adder architectures including parallel prefix adders and their comparative analysis based on ultimate performance parameters-area and power at 28nm technology. All designs have been synthesized and implemented on Xilinx Zynq-7000 FPGA board using Xilinx Vivado 14.4 design tool. Verilog HDL is used as programming HDL. Weinberger adder provides the best area solution.
Smart Homes and Quality of Life for the Elderly: Perspective of Competing Models
(2018)

The percentage of the elderly population has been on rise steadily over the past few years across the globe, which is a cause of serious concern among the research fraternity. A lot of research is going on that tries to harness the benefits of the various information and communication technologies to enable these elderly people to live independently and promote a sense of overall well-being. Although in the evolutionary phase, yet smart homes can help these elderly people in their daily life. However, for the success of such smart systems, the intention of the users towards using those systems must be understood. But, there is a serious lack of relevant exploratory research that tries to measure and explain the adoption of such smart homes by the elderly population from a multiple theory perspective: namely, the technology acceptance model, the theory of reasoned action and the theory of planned behavior. The main aim of this paper is to fill up this void of a lack of theoretical approach by testing the three models in the context of the adoption of smart homes by the elderly population. In order to do so, we conducted a survey with  $N = 239$  (after screening) and analyzed the results using the structural equation modeling and confirmatory factor analysis techniques. Results suggest that all the three models are valid although they do not take into account certain factors that are unique to this context. This paper paper provides the initial groundwork to explore the process of adopting smart home services by the elderly with potential future research areas.
Scaling Scrum in a Large Distributed Project
(2011)

This paper presents a currently ongoing single case study on adopting and scaling Scrum in a large software development project distributed across four sites. The data was gathered by 19 semi-structured interviews of project personnel, including managers, architects, developers and testers. At the time of the interviews the project had grown in size during the past 2,5 years from two collocated Scrum teams to 20 teams located in four countries and employing over 170 persons. In this paper we first describe our research approach and goals. Then we briefly summarize the preliminary results of this ongoing study: we explain how Scrum practices were scaled, as well as discuss the successes and challenges experienced when adopting the agile practices and scaling them, while at the same time growing the project size at a fast pace. Even though this project has been very successful from the business point of view, it has experienced a lot of problems in applying Scrum, especially related to scaling the agile practices. Thus, it seems that adapting Scrum practices to work well in a large distributed setting is challenging.
The Statistical Recurrent Unit
(2017)

Sophisticated gated recurrent neural network architectures like LSTMs and GRUs have been shown to be highly effective in a myriad of applications. We develop an un-gated unit, the statistical recurrent unit (SRU), that is able to learn long term dependencies in data by only keeping moving averages of statistics. The SRU’s architecture is simple, un-gated, and contains a comparable number of parameters to LSTMs; yet, SRUs perform favorably to more sophisticated LSTM and GRU alternatives, often outperforming one or both in various tasks. We show the efficacy of SRUs as compared to LSTMs and GRUs in an unbiased manner by optimizing respective architectures’ hyperparameters in a Bayesian optimization scheme for both synthetic and realworld tasks.
Flight Dynamics Modeling of Dual-Spin Guided Projectiles
(2017)

This paper presents a complete nonlinear parameter-dependent mathematical model, as well as a procedure for computing the quasi-linear parameter-varying (q-LPV) model of a class of spin-stabilized canard-controlled guided projectiles. The proposed projectile concept possesses a so-called dual-spin configuration: the forward section contains the necessary control and guidance software and hardware, whereas the aft roll-decoupled and rapidly spinning section contains the payload. Wind-axes instead of body-axes variables, as is the case in the existing literature, are preferred for the modeling of the highly coupled pitch/yaw airframe nonlinear dynamics, since they are better suited to control synthesis. A q-LPV model, approximating these nonlinear dynamics around the system equilibrium manifold and capturing their dependence on diverse varying flight parameters, is analytically obtained. In addition, a detailed stability analysis specific to this kind of weapons is performed throughout their large flight envelope using the aforementioned q-LPV model. Furthermore, a study is conducted in order to quantify the influence of reducing the dimension of the flight parameter vector on the exactness of the q-LPV model. Finally, the critical influence on the pitch/yaw-dynamics of the nose-embedded sensor position, and of uncertainty on the various static and dynamic aerodynamic coefficients as well as the aerodynamic angles, is shown.
Information Retrieval
(2017)

Chinese input recommendation plays an important role in alleviating human cost in typing Chinese words, especially in the scenario of mobile applications. The fundamental problem is to predict the conditional probability of the next word given the sequence of previous words. Therefore, statistical language models, i.e. n-grams based models, have been extensively used on this task in real application. However, the characteristics of extremely different typing behaviors usually lead to serious sparsity problem, even n-gram with smoothing will fail. A reasonable approach to tackle this problem is to use the recently proposed neural models, such as probabilistic neural language model, recurrent neural network and word2vec. They can leverage more semantically similar words for estimating the probability. However, there is no conclusion on which approach of the two will work better in real application. In this paper, we conduct an extensive empirical study to show the differences between statistical and neural language models. The experimental results show that the two different approach have individual advantages, and a hybrid approach will bring a significant improvement.
On Trade-offs of Applying Block Chains for Electronic Voting Bulletin Boards
(2018)

This paper takes a critical look at the recent trend of building electronic voting systems on top of block chain technology. Even though being very appealing from the election integrity perspective, block chains have numerous technical, economical and even political drawbacks that need to be taken into account. Selecting a good trade-off between desirable properties and restrictions imposed by different block chain implementations is a highly non-trivial task. This paper aims at bringing some clarity into performing this task. We will mostly be concentrating on public permissionless block chains and their applications as bulletin board implementations as these are the favourite choices in majority of the recent block chain based voting protocol proposals.
Q-Learning-Based Power Control for LTE Enterprise Femtocell Networks
(2017)

Small cells are beneficial for both users and operators to improve communication quality. The femtocell is one type of small cell used for indoor coverage. Enterprise femtocell installation inevitably produces intralayer and interlayer interference management issues. Enterprise femtocell optimization and indoor propagation modeling are built in this work based on the communications environment characteristics. Q-learning-based distributed and hybrid power control strategies are then proposed. The throughput, energy efficiency, and user experience satisfaction with conventional scheduling methods are compared. The simulation results show that distributed Q-learning performs better local optimization while hybrid Q-learning enhances global performance.
Discovering the invisible city: Location-based games for learning in smart cities
(2013)

In this paper we discuss how location-based mobile games can be designed for learning in modern technology enhanced public spaces. We start with the description of the design process and we identify the main challenges faced. We elaborate the case of the game Invisible City: Rebels vs. Spies, a game to be played in a city centre using mobile devices. Through this case we highlight the adaptation of an original party game into a mobile form, the issues we faced and the key aspects conductive to learning in a smart city. It is claimed that creating mobile city games for learning is a new challenge, as our city landscapes are augmented with an increasing number of layers of digital information in which a new generation of city games are played.
Security and Privacy in Cloud Computing: Vision, Trends, and Challenges
(2015)

Cloud computing offers cost-effective solutions via a variety of flexible services. However, security concerns related to managing data, applications, and interactions hamper the rapid deployment of cloud-based services on a large scale. Although many solutions exist, efficiency, scalability, and provable security still have issues that need to be properly addressed. This article explores the various challenges, existing solutions, and limitations of cloud security, with a focus on data utilization management aspects, including data storage, data analytics, and access control. The article concludes with a discussion on future research directions that might lead to more trustworthy cloud security and privacy.
The sequential maximum angle convex cone ( SMACC ) endmember model
(2004)

A new endmember extraction method has been developed that is based on a convex cone model for representing vector data. The endmembers are selected directly from the data set. The algorithm for finding the endmembers is sequential: the convex cone model starts with a single endmember and increases incrementally in dimension. Abundance maps are simultaneously generated and updated at each step. A new endmember is identified based on the angle it makes with the existing cone. The data vector making the maximum angle with the existing cone is chosen as the next endmember to add to enlarge the endmember set. The algorithm updates the abundances of previous endmembers and ensures that the abundances of previous and current endmembers remain positive or zero. The algorithm terminates when all of the data vectors are within the convex cone, to some tolerance. The method offers advantages for hyperspectral data sets where high correlation among channels and pixels can impair un-mixing by standard techniques. The method can also be applied as a band-selection tool, finding end-images that are unique and forming a convex cone for modeling the remaining hyperspectral channels. The method is described and applied to hyperspectral data sets.
A survey: Attacks on RPL and 6LoWPAN in IoT
(2015)

6LoWPAN (IPv6 over Low-Power Wireless Personal Area Networks) standard allows heavily constrained devices to connect to IPv6 networks. 6LoWPAN is novel IPv6 header compression protocol, it may go easily under attack. Internet of Things consist of devices which are limited in resource like battery powered, memory and processing capability etc. for this a new network layer routing protocol is designed called RPL (Routing Protocol for low power Lossy network). RPL is light weight protocol and doesn't have the functionality like of traditional routing protocols. This rank based routing protocol may goes under attack. Providing security in Internet of Things is challenging as the devices are connected to the unsecured Internet, limited resources, the communication links are lossy and set of novel technologies used such as RPL, 6LoWPAN etc. This paper focus on possible attacks on RPL and 6LoWPAN network, counter measure against them and consequences on network parameters. Along with comparative analysis of methods to mitigate these attacks are done and finally the research opportunities in network layer security are discussed.
Imbalanced-learn: A Python Toolbox to Tackle the Curse of Imbalanced Datasets in Machine Learning
(2017)

imbalanced-learn is an open-source python toolbox aiming at providing a wide range of methods to cope with the problem of imbalanced dataset frequently encountered in machine learning and pattern recognition. The implemented state-of-the-art methods can be categorized into 4 groups: (i) under-sampling, (ii) over-sampling, (iii) combination of overand under-sampling, and (iv) ensemble learning methods. The proposed toolbox depends only on numpy, scipy, and scikit-learn and is distributed under MIT license. Furthermore, it is fully compatible with scikit-learn and is part of the scikit-learn-contrib supported project. Documentation, unit tests as well as integration tests are provided to ease usage and contribution. Source code, binaries, and documentation can be downloaded from https://github.com/scikit-learn-contrib/imbalanced-learn.
Function-Private Functional Encryption in the Private-Key Setting
(2014)

Functional encryption supports restricted decryption keys that allow users to learn specific functions of the encrypted messages. Although the vast majority of research on functional encryption has so far focused on the privacy of the encrypted messages, in many realistic scenarios it is crucial to offer privacy also for the functions for which decryption keys are provided. Whereas function privacy is inherently limited in the public-key setting, in the private-key setting it has a tremendous potential. Specifically, one can hope to construct schemes where encryptions of messages $$\mathsf{m}_1, \ldots , \mathsf{m}_T$$ m 1 , … , m T together with decryption keys corresponding to functions $$f_1, \ldots , f_T$$ f 1 , … , f T , reveal essentially no information other than the values $$\{ f_i(\mathsf{m}_j)\}_{i,j\in [T]}$$ { f i ( m j ) } i , j ∈ [ T ] . Despite its great potential, the known function-private private-key schemes either support rather limited families of functions (such as inner products) or offer somewhat weak notions of function privacy. We present a generic transformation that yields a function-private functional encryption scheme, starting with any non-function-private scheme for a sufficiently rich function class. Our transformation preserves the message privacy of the underlying scheme and can be instantiated using a variety of existing schemes. Plugging in known constructions of functional encryption schemes, we obtain function-private schemes based either on the learning with errors assumption, on obfuscation assumptions, on simple multilinear-maps assumptions, and even on the existence of any one-way function (offering various trade-offs between security and efficiency).
Dermoscopy: basic concepts.
(2008)

Dermoscopy is a very useful technique for the analysis of pigmented skin lesions. It represents a link between clinical and histological views, permitting an earlier diagnosis of skin melanoma. It also helps in the diagnosis of many other pigmented skin lesions, such as seborrheic keratosis, pigmented basal cell carcinoma, hemangioma, blue nevus, atypical nevus, and mole, which can often clinically simulate melanoma. In this article, dermoscopy is reviewed from its history to the basic concepts of the interpretation of dermoscopic images. The goal is to introduce this subject to those not yet familiar with it, in order to instigate and encourage the training and practice of this technique of growing importance for everyday usage.
Deep learning to classify difference image for image change detection
(2014)

Image change detection is a process to analyze multi-temproal images of the same scene for identifying the changes that have occurred. In this paper, we propose a novel difference image analysis approach based on deep neural networks for image change detection problems. The deep neural network learning algorithm for classification includes unsupervised feature learning and supervised fine-tuning. Some samples with the labels of high accuracy obtained by a pre-classification are used for fine-tuning. Since a deep neural network can learn complicated functions that can represent high-level abstractions, it can obtain satisfactory results. Theoretical analysis and experiment results on real datasets show that the proposed method outperforms some other methods.
Category fluency test: normative data for English- and Spanish-speaking elderly.
(2000)

Category fluency tasks are an important component of neuropsychological assessment, especially when evaluating for dementia syndromes. The growth in the number of Spanish-speaking elderly in the United States has increased the need for appropriate neuropsychological measures and normative data for this population. This study provides norms for English and Spanish speakers, over the age of 50, on 3 frequently used measures of category fluency: animals, vegetables, and fruits. In addition, it examines the impact of age, education, gender, language, and depressed mood on total fluency scores and on scores on each of these fluency measures. A sample of 702 cognitively intact elderly, 424 English speakers, and 278 Spanish speakers, participated in the study. Normative data are provided stratified by language, age, education, and gender. Results evidence that regardless of the primary language of the examinee, age, education, and gender are the strongest predictors of total category fluency scores, with gender being the best predictor of performance after adjusting for age and education. English and Spanish speakers obtained similar scores on animal and fruit fluency, but English speakers generated more vegetable exemplars than Spanish speakers. Results also indicate that different fluency measures are affected by various factors to different degrees.
Towards subjective quality of experience assessment for omnidirectional video streaming
(2017)

Currently, we witness dramatically increasing interest in immersive media technologies like Virtual Reality (VR), particularly in omnidirectional video (OV) streaming. Omnidirectional (also called 360-degree) videos are panoramic spherical videos in which the user can look around during playback and which therefore can be understood as hybrids between traditional movie streaming and interactive VR worlds. Unfortunately, streaming this kind of content is extremely bandwidth intensive (compared to traditional 2D video) and therefore, Quality of Experience (QoE) tends to deteriorate significantly in absence of continuous optimal bandwidth conditions. In this paper, we present a first approach towards subjective QoE assessment for omnidirectional video (OV) streaming. We present the results of a lab study on the QoE impact of stalling in the context of OV streaming using head-mounted displays (HMDs). Our findings show that subjective testing for immersive media like OV is not trivial, with even simple cases like stalling leading to unexpected results. After a discussion of characteristic pitfalls and lessons learned, we provide a a set of recommendations for upcoming OV assessment studies.
Three Decades of Driver Assistance Systems: Review and Future Perspectives
(2014)

This contribution provides a review of fundamental goals, development and future perspectives of driver assistance systems. Mobility is a fundamental desire of mankind. Virtually any society strives for safe and efficient mobility at low ecological and economic costs. Nevertheless, its technical implementation significantly differs among societies, depending on their culture and their degree of industrialization. A potential evolutionary roadmap for driver assistance systems is discussed. Emerging from systems based on proprioceptive sensors, such as ABS or ESC, we review the progress incented by the use of exteroceptive sensors such as radar, video, or lidar. While the ultimate goal of automated and cooperative traffic still remains a vision of the future, intermediate steps towards that aim can be realized through systems that mitigate or avoid collisions in selected driving situations. Research extends the state-of-the-art in automated driving in urban traffic and in cooperative driving, the latter addressing communication and collaboration between different vehicles, as well as cooperative vehicle operation by its driver and its machine intelligence. These steps are considered important for the interim period, until reliable unsupervised automated driving for all conceivable traffic situations becomes available. The prospective evolution of driver assistance systems will be stimulated by several technological, societal and market trends. The paper closes with a view on current research fields.
USARSim: a robot simulator for research and education
(2007)

This paper presents USARSim, an open source high fidelity robot simulator that can be used both for research and education. USARSim offers many characteristics that differentiate it from most existing simulators. Most notably, it constitutes the simulation engine used to run the virtual robots competition within the Robocup initiative. We describe its general architecture, describe examples of utilization, and provide a comprehensive overview for those interested in robot simulations for education, research and competitions.
Noise and the Reality Gap: The Use of Simulation in Evolutionary Robotics
(1995)

The pitfalls of naive robot simulations have been recognised for areas such as evolutionary robotics. It has been suggested that carefully validated ispell slides.tex simulations with a proper treatment of noise may overcome these problems. This paper reports the results of experiments intended to test some of these claims. A simulation was constructed of a two-wheeled Khepera robot with IR and ambient light sensors. This included detailed mathematical models of the robot-environment interaction dynamics with empirically determined parameters. Artiicial evolution was used to develop recurrent dynamical network controllers for the simulated robot, for obstacle-avoidance and light-seeking tasks, using diierent levels of noise in the simulation. The evolved controllers were down-loaded onto the real robot and the correspondence between behaviour in simulation and in reality was tested. The level of correspondence varied according to how much noise was used in the simulation, with very good results achieved when realistic quantities were applied. It has been demonstrated that it is possible to develop successful robot controllers in simulation that generate almost identical behaviours in reality, at least for a particular class of robot-environment interaction dynamics.